docs/tutorials/rag/

[giscus[bot]]

docs/tutorials/rag/

One of the most powerful applications enabled by LLMs is sophisticated question-answering (Q&A) chatbots. These are applications that can answer questions about specific source information. These applications use a technique known as Retrieval Augmented Generation, or RAG.

https://python.langchain.com/docs/tutorials/rag/

[ksairos]

If you're using Chroma during the query analysis, don't forget to change the filter for vector_store.similarity_search() function in the def retrieve(state: State) node.

Instead of
filter=lambda doc: doc.metadata.get("section") == query["section"], which causes a Expected type 'dict[str, str] | None', got '(doc: Any) -> bool' instead error, you can use {"section": query["section"]}.